CHAPTER – 2
Chapter 2
Literature Review
This chapter presents a review of literature related to the current research. In order to
provide background information and justification for the research framework, the first
section begins with an overview and discussion of business models. In this section,
definition, configuration, approaches towards constructing a generic business model
framework are presented followed by a discussion of contextual factors influencing
the development and use of business models.
The second part discussed critical success factors, their essentiality and methods on
how critical success factors are identified. Next we discuss business performance
measurement systems from the performance measurement literature and then the
chapter moves towards selection of a business performance measure.
A discussion of resource based theory is also provided to support the relationship
between organizational learning and organization performance. Subsequently, the
chapter continues with a discussion and recommendations related to study of
organizational performance. The chapter ends with a brief summary of the research
model.
2.1. Business Models
2.1.1. Background
The first systematic and comparative account of growth and change in the modern
industrial corporation was presented by Alfred Chandler in his seminal Strategy and
Structure (Chandler, 1962). He showed challenges of diversity implicit in a strategy of
growth called for imaginative responses in administration of the enterprise. In his
subsequent work, Chandler (1990) also showed how scale and scope economies
provided new growth opportunities for the enterprise during the second industrial
revolution. Chandler (1990) research question in part is as follows, ‘It then becomes
critical to explain how and why the institution [of the modern industrial firm] grew by
adding new units—units that carried out different economic functions, operated in
different geographical regions, and handled different lines of products.’ Later in the
14
volume, he includes the introduction of new products, based on internal research and
technology, as part of this definition.
The ideas from Strategy and Structure was built upon and applied to emerging
concepts of corporate strategy by Ansoff (1965). Strategy came to be seen as a
conscious plan to align the firm with opportunities and threats posed by its
environment. Andrews (1987) was one of the first theorists to differentiate between a
business strategy and a corporate strategy. He held the former to be ‘the productmarket choices made by division or product line management in a diversified
company’ and that corporate strategy was a superset of business strategy. Like
business strategy, corporate strategy defines products and markets— and determines
the company’s course into the almost indefinite future. He also indicates that a
company will have only one corporate strategy but may incorporate several business
strategies into it. Thus, a firm’s current businesses influenced its choice of likely
future businesses as well.
While the notion of strategy was subsequently developed in different directions, one
branch of its development was to research into how managers could leverage the
resources of the organization beyond that organization’s current business. Early work
started from a cognitive model of rational calculation and full information. Teece
(1982) built a framework where a firm’s underutilized resources, combined with
imperfections in the markets, conferred advantage for diversification moves to the
organization. Empirical evidence has shown how a firm’s technological position
helped it enter nearby business areas, because experience in ‘related’ technologies
reduced the costs of entering into adjacent areas (Teece et al., 1993; Silverman, 1999).
Mintzberg (1994) identified the ‘emergent’ character of many successful strategies,
and emphasized the importance of adaptation over planning while Burgelman (1983)
developed a process model for how a firm can enact strategic change based on
managing limited information.
A later branch of the strategy literature incorporated cognitive bias into the idea of
strategy. Prahalad and Bettis (1986) introduced the notion of a dominant logic: a set
of heuristic rules, norms and beliefs that managers create to guide their actions. This
logic usefully focuses managers’ attention, as they seek new opportunities for the
firm. Empirical examples of this path-dependent behaviour can be found in
15
semiconductor equipment (Henderson and Clark, 1990), disk drives (Christensen,
1997) and typesetting (Tripsas, 1997). Some scholars conclude that firms may indeed
develop the ability to manage new technological opportunities effectively if they
invest in integrative capabilities (Henderson, 1994), ambidextrous internal processes
(Tushman and O’Reilly, 1997) or complementary assets (Tripsas, 1997). Other
scholars believe that the firm must avoid internal resource allocation processes, and
manage disruptive technologies outside the main business (e.g. Christensen, 1997).
Chesbrough & Rosenbloom (2002), contribute to this literature by offering the
business model as a construct that can inform these earlier perspectives. He indicates
that the business model provides a coherent framework that takes various
organizational characteristics and potentials as inputs, and converts them through
customers and markets into economic outputs. So, the business model is thus
conceived as a focusing device that mediates between technology development and
economic value creation. They also indicate that the failure of firms to manage
effectively in the face of technological change can be understood as the difficulty
these firms have in perceiving and then enacting new business models, when
technological change requires it. They also argue that firms need to understand the
cognitive role of the business model, in order to commercialize technology in ways
that will allow firms to capture value from their technology investments, when
opportunities presented by its technologies do not fit well with the firm’s current
business model.
Chesbrough & Rosenbloom (2002) contrast the concept of business model to that of
strategy by identifying the following three differences:
·
Creating value vs. capturing value – the business model focus is on value
creation. While the business model also addresses how that value will be
captured by the firm, strategy goes further by focusing on building a
sustainable competitive advantage.
·
Business value – the business model is an architecture for creating an
economic value for the business.
·
Assumed knowledge levels – the business model assumes a limited
environmental knowledge, whereas strategy depends on a more complex
analysis that requires more certainty in the knowledge of the environment.
16
2.1.2. Definition and Application
For a systematic study of business models, we need to define business models and
distinguish their different types. But before digging into the definitions of the
expression business model, according to Osterwalder et al., (2005) both business and
model, by themselves have a specific meaning. They interpret the world model as "a
simplified description and representation of a complex entity or process".
Representation implying conceptualization, which is described as “the objects,
concepts and other entities that are assumed to exist in some area of interest and their
inter-relationship according to Genesereth and Nilsson (1987). Putting both these
elements together Osterwalder et al., (2005) propose that the reflection on the
business model concept must go in the following direction:
“A business model is a conceptual tool containing a set of objects, concepts and their
relationships with the objective to express the business logic of a specific firm.
Therefore we must consider which concepts and relationships allow a simplified
description and representation of what value is provided to customers, how this is
done and with which financial consequences.”
In their opinion, the above definition is sufficiently broad to embrace the different
reflections on business models that have sprung up in different fields such as ebusiness, IS, computer science, strategy or management (Pateli and Giaglis, 2003).
A review of the literature using the term business model shows that there exists a
continuum between authors using the term to simply refer to the way a company does
business Galper (2001), Gebauer and Ginsburg (2003) and authors that emphasize the
model aspect Gordijn (2002). These two viewpoints differ because the former
generically refers to the way a company does business; whereas the latter refers to a
conceptualization of the way a company does business in order to reduce complexity
to an understandable level. In other words, for business models, the quest is to
identify the elements and relationships that describe the business a company does.
Thus, the business model concept can best be understood as a conceptual view of a
particular aspect of a specific company.
According to Magretta (2003) a business model in essence, is a theory that is
continually being tested in the marketplace. Grasl (2008) defines a business model as
17
a set of assumptions about how an organization will perform by creating value for all
the players on whom it depends, not just its customers.
According to Amit & Zott (2001) in their search for wealth creation, one of the main
challenges of entrepreneurs/organizations is the identification or creation of wealth
producing opportunities, and the ways to profitably capture these opportunities in an
uncertain environment. To do so, entrepreneurs/organizations design a business
model, namely the ways their new business is going to transact with, and relate to
suppliers, customers, and partners. They view the business model as depicting “the
content, structure, and governance of transactions designed so as to create value
through the exploitation of business opportunities.” The above indicated authors along
with Magretta (2002), Ghaziani and Ventresca (2002) recognize business model
design as a crucial task for entrepreneurs.
Malone et al., (2006) offer an operational definition, based on two fundamental
dimensions of what a business does. The first dimension considers what types of
rights are being sold, arrived at after classifying a business as Creator, Distributor,
Landlord, or Broker. The second dimension considers what type of assets is involved.
In this case, they distinguish among four important asset types: physical, financial,
intangible, and human. According to them a combination of the indicated two
dimensions leads to sixteen detailed business models.
Timmers (1998) defines a business model as including an architecture for the product,
service, and information flows, a description of the benefits for the business actors
involved, and a description of the sources of revenue.
Tapscott, et al., (2000) focus on the system of suppliers, distributors, commerce
service providers, infrastructure providers, and customers, labelling this system the
business web or “b-web.” They differentiate business webs along two dimensions:
control (from self-control to hierarchical) and value integration (from high to low).
Weill and Vitale (2001) include “roles and relationships among a firm’s customers,
allies, and suppliers, major flows of product, information, and money, and major
benefits to participants” in their definition of a business model. They describe eight
atomic e-business models, each of which can be implemented as a pure e-business
model or combined to create a hybrid model.
18
Rappa (2003) defines a business model as “the method of doing business by which a
firm can sustain itself” and notes that the business model is clear about how a firm
generates revenues and where it is positioned in the value chain.
Other definitions of business models emphasize the connections a business model
provides between technical potential and the realization of economic value
(Chesbrough and Rosenbloom, 2002), the design of the transactions of a firm in
creating value (Amit and Zott, 2001), the blend of the value stream for buyers and
partners, the revenue stream, and the logical stream (the design of the supply chain)
(Mahadevan, 2000), and the firm’s core logic for creating value (Linder and Cantrell,
2000). In an attempt to integrate these definitions, Osterwalder, et al., (2002) proposes
an e-business framework with four pillars: the products and services a firm offers, the
infrastructure and network of partners, the customer relationship capital, and the
financial aspects.
Common to all of these definitions of business and e-business models is an emphasis
on how a firm makes money. Magretta (2002) argues that the strength of a business
model is that it tells a story about the business, focusing attention on how pieces of
the business fit together—with the strategy describing how the firm differentiates
itself and deals with competition. The idea of business model is also consistent with
the work on interdependencies (Levinthal, 1997).
In summary, the definitions for business models range from generic (Magretta, 2002;
Petrovic et al., 2001) to more concrete ones (Timmers, 1998; Weill & Vitale, 2001;
Osterwalder & Pigneur, 2002). Thus, we can find definitions that explain what the
purpose of a business model is, while other definitions focus on specifying its primary
elements, and possibly their interrelationships.
Considering and amalgamating the various definitions for business models in the
literature, this study defines it as:
“A business model is an essential conceptual structure that contains a set of elements
(critical success factors) and their relationships that allows expressing an
organization's unique strengths required to attain business success.”
19
It is also a description of the value an organization offers to its stakeholders, its
network of partners for creating, marketing and delivering this value, the inherent
architecture of the firm and the relationships between these that affect the
organization’s business performance or success.
2.1.3. Generic Business Model – Life Sciences BPO Industry
As explained in Chapter 1, affordable information technology innovation and
advancement specially in the internet domain has triggered the phenomenon of
organizations attaining business competitiveness through outsourcing. Due to this
phenomenon of outsourcing business processes, triggered by advances in information
technology advancements there has been an increase in the possible business
configurations a company can adopt because of the reduced coordination and
transaction costs (Williamson, 1975).
In other words, organizations can increasingly work in partnerships, offer joint value
propositions, build-up multi-channel and multi-owned distribution networks and
profit from diversified and shared revenue streams. The downside of this is that a
company's business has more stakeholders, becomes more complex and is harder to
understand and communicate. If this assumption is true one can argue that the existing
management concepts and tools may not be sufficient anymore and that new ones
have to be found. For example, Rentmeister and Klein (2003) call for new modelling
methods in the domain of business models. Effectively, a whole range of authors
propose using the relatively new concept of business models for managing companies
in this new business era (Chesbrough and Rosenbloom, 2000; Afuah and Tucci, 2001;
Applegate, 2001; Pateli and Giaglis, 2003).
This research study is part of this new research stream on business models and
focuses on a specific area not covered so well until now: specifying, conceptualizing
business models, understanding the effect of business models on business
performance. Most business model research stays at a non-conceptual, broad and
sometimes even vague level and hence this work tries to dig into the details and
define a generic model to describe business models and their effect on business
performance / success. This approach becomes indispensable if one wants to provide
20
effective
business
model
framework
to
improve,
manage
business
performance/success in a rapidly moving, complex and uncertain business
environment of the Life Sciences BPO industry domain.
Based on the above, for the creation of a generic business model or framework which
would define the elements and their relationship affecting business performance of the
Indian Life Sciences BPO Industry, the work of Ushold and King (1996) was referred
to and adapted. In the general the outline for the process was:
·
Identification of the key elements (constructs or elemental critical success
factors) and their relationships in the domain of interest (i.e. scoping the
domain of business models)
·
Production of precise unambiguous text definitions for such elements,
concepts and or relationships
·
Identification of terms and themes to refer to such concepts and or
relationships
·
Agreeing on all of the above
A partial outcome of this research is a generic business model framework specific to
the Life Sciences BPO industry that shall ideally represent the foundation for new
management tools in business performance assessment and business strategy.
2.1.4. Business Model Constructs
Constructs or elements or concepts or critical success factors form the vocabulary of a
domain. They constitute a conceptualization used to describe problems within a
domain. A model is a set of propositions or statements expressing relationships
among constructs. Models represent situations as problem and solution statements
whereas a method is a set of steps (guidelines) used to perform a particular task.
Methods are based on a set of underlying constructs (elements) and a representation
(model) of their relationships in a particular domain.
March and Smith (1995) identify “build” and “evaluate” as the two main issues in
constructing a model. Build refers to the construction of constructs, models and
21
methods demonstrating that they can be constructed. Evaluate refers to the
development of criteria and the assessment of the output's performance against those
criteria. Parallel to these two research activities March and Smith add the natural and
social science couple, which are theorize and justify. This refers to the construction of
theories that explain how or why something happens. Justify refers to theory proving
and requires the gathering of scientific evidence that supports or refutes the theory.
Summarized, constructs, models, and methods are built to perform a particular task.
These outputs then become the object of study, which must be evaluated
scientifically. They have to be evaluated in order to conclude if any progress has been
made. In order to do this, we have to develop metrics and measure the outputs
according to those metrics. For instance, when an artefact has been applied in a
specific environment, it is important to determine why and how the artefact worked or
did not work - theorize. Then, given a generalization or theory we must justify that
explanation by gathering evidence to test the theory in question. Justification
generally follows the natural science methodologies governing data collection and
analysis.
According to Rugman and Verbeke (2000), the “five forces model” for industry
analysis (Porter, 1980) is a standard tool used by both academics and practitioners
when conducting strategic management studies.
Porter (2004) puts forth that competition in an industry is rooted in its underlying
economic structure and goes well beyond the behaviour of current competitors. He
also proposes that competition in an industry depends on five basic competitive forces
– Bargaining Power of suppliers, customers, Threat of new entrant, Threat of
Substitutes, and Industry Rivalry (key structural features of the industry). This
framework provides a structural analysis mechanism which is the fundamental step
and a key building block in diagnosing industry competition in any country or in an
international market.
An important extension to Porter’s work is found in the work of Brandenburger and
Nalebuff (1995) in the mid-1990s. Using game theory, they added the concept of
complementors (also called "the 6th force" a term which was coined by Andrew
Grove, former CEO of Intel), helping to explain the reasoning behind strategic
alliances.
22
Complementors are a very visible and influencing force in the globalized, competitive
arena due to their inherent nature of “synergic value addition” to the core product or
services of a supplier. It is a term used to describe businesses that sell a product/s or
service/s that complement the product or service of another organization by adding
value to them; for example, Intel and Microsoft (Pentium processors and Windows).
Figure 2.1 depicts a visual representation of the “Six Forces Model” given below.
Figure 2.1 - The “Six Forces Model”
This approach was used along with others described below to have an initial insight
into the constructs which influence business performance of the Life Sciences BPO
Industry. On applying this analysis it was determined that – threat of substitutes are
low, threat of new entrants is low due to high entry barriers, exit barriers are also low
and competitive rivalry within the industry is also low since each of the player in this
industry is still trying out various strategies and hence rules of engagement are not yet
clearly defined.
Coupling this with a relatively higher bargaining power of suppliers compared to
bargaining power of buyers and low bargaining power of complementors, we can
conclude that, at this point in time, the Life Science BPO industry environment
exhibits and facilitates a highly sustainable, high profitability scenario and is a very
attractive segment for incubating new businesses, creating Pharma focused industry
segments or creating new profitability, business models.
23
According to Shank & Govindarajan (1993) value chain analysis is undertaken in
order to understand the behavior of costs and the sources of differentiation in an
industry segment. The value chain framework is an approach for breaking down the
sequence (chain) of business functions into strategically relevant activities through
which utility / value is added to products and services. On completion of this analysis,
the following structure represented under Figure 2.2 presented below can be
constructed.
Figure 2.2 - Life Sciences BPO industry value chain and Market map
Further, as an extension to the value chain analysis Matthias and Frits (2001) tend to
answer in their paper “Successful Build-to-Order Strategies Start with the Customer”
the question – How holistic value chain strategies can be leveraged to enhances
responsiveness to customer requirements/needs? and thereby argue that it is essential
to see value creation as multidirectional rather than linear. Hence Frits and Matthias
(2006) propose the notion of a “value grid” which has a multidimensional approach
compared to the linear approach which the value chain analysis takes to understand
the various value adding components, systems and their relationships.
2.1.5. Business Model Design Themes
Configuration theory provides a useful basis from which to evaluate different business
model designs by considering holistic configurations, of design elements (Miles and
Snow, 1978; Mintzberg, 1979). Configurations are constellations of design elements
that commonly occur together because their interdependence makes them fall into
24
patterns (Meyer, Tsui and Hinings, 1993). The design elements of a business model
are the content, structure, and governance of transactions that serve the focal firm to
pursue, and exploit business opportunities. In this study, we follow Miller’s (1996)
suggestion to study configuration as a variable rather than as a deviation from an ideal
type (Doty, Glick, and Huber, 1993). Miller (1996) states that, “Configuration…can
be defined as the degree to which an organization’s elements are orchestrated and
connected by a single theme”.
2.2. Critical Success Factors (CSFs)
Spector (1992) recommends that researchers should first clearly define the construct
/framework/phenomenon based on theory, and then develop items that support the
definition, and take a confirmatory approach to validate the theoretical ideas guiding
the creation of items. In addition, when working with a complex construct, Spector
(1992) also recommends that researchers should partition the construct into several
key dimensions to ensure the adequacy of the content domain and develop a scale
with multiple subscales by creating items for each separate dimension of the
construct.
Spector’s (1992) recommendation was implemented by utilising the method of
Critical Success Factor identification and analysis was utilised to identify, categorise
and depict the relationships between these Constructs or elements or concepts or
critical success factors which influence business performance of organizations in the
Life Sciences BPO Industry.
Critical success factors (CSFs) have been used significantly to present or identify a
few key factors that organizations should focus on to be successful. As a definition,
critical success factors refer to "the limited number of areas in which satisfactory
results will ensure successful competitive performance for the individual, department,
or organization” (Rockart and Bullen, 1986). In Rockhart’s (1979) seminal work
surrounding CSFs from the viewpoint of chief executives, he states that the process of
identifying CSFs helps to ensure that those factors receive the necessary attention. He
further proposes that the procedure allows for clear definition of the type of
information that the company needs and moves away from the trap of building a
25
system around data that are easy to collect. Rockhart’s (1979) work was based on
research by D. Ronald Daniel, who was, according to Rockhart, the first person to
discuss “success factors” in the management literature.
In Rockhart’s view, CSFs were those specifically distinguished areas that an
organization needed to “get right” in order for the business to successfully compete.
Based on this, identifying CSFs becomes critical as it allows firms to focus their
efforts on building their capabilities to meet the CSFs, or even allow firms to decide if
they have the capability to build the requirements necessary to meet CSFs and hence
control business performance rather than the other way around.
Success factors were already being used as a term in management when Rockart and
Bullen reintroduced the concept to provide greater understanding of the concept and,
at the same time, give greater clarity of how CSFs can be identified. CSFs are
primarily tailored to a firm's particular situation as different situations (e.g. industry,
division, individual) lead to different critical success factors. Rockart and Bullen
presented five key sources of CSFs: the industry, competitive strategy and industry
position, environmental factors, temporal factors, and managerial position (if
considered from an individual's point of view).
While Rockart and Bullen define the structured interview as the key method for
identifying CSFs at the individual level, there are other methods that have been used
and have been found to be effective in identifying them. These other methods have
been identified as action research, case studies, Delphi technique, group interviewing,
literature review, etc. Also, in selecting names to identify each category, an attempt
should be made to make the name graphic enough to allow the reader to determine its
referent.
According to literature, for the organization pursuing the CSF method, the foundation
for writing good CSFs is a good understanding of the environment, the industry and
the organization. In order to do so, this requires the use of information that is readily
available in the public domain. Externally, industry information can be sourced from
industry associations, news articles, trade associations, prospectuses of competitors,
and equity/analyst reports. Other sources which would be helpful are interviews with
26
buyers and suppliers, industry experts and independent observers. These would all be
helpful in building knowledge of the environment, the industry and competitors.
Extensive search and review of a large number of journals, publications, industry
reports using keywords identified in a preliminary literature review was undertaken to
identify CSF’s specific to the study. Successive rounds of article abstract reviews
resulted in identifying quite a number of articles that could guide the development of
a theoretical definition of the Business Model construct – in general. But there were
only a few articles which could guide the development of a theoretical definition of
the Business Model construct specific to the Life Sciences BPO industry. Table 2.1
given below presents an overview of the literature review protocol.
Table 2.1 - Literature Review Protocol
Sl.
Particulars
1.
Purpose
2.
Search Strategy
3.
Exclusion Criteria
4.
Keywords
5.
Databases
Description
· To identify existing Business Model Elements, Business
Models, dependence of Business Performance on Business
Models – if any in the Indian Life Sciences Business Process
Outsourcing (BPO) Industry
· Search by specific keywords
· Duplicate references from the search were discarded
· Potential cross-references including not only journal articles
but also books, books chapters, conference papers and working
papers were identified whilst reading these articles
· An article will be excluded from the systematic review if the
following criteria is met :
§ The majority of the article does not address the above
identified purpose
· “Business Model Elements”, “Business Models”, “dependence
of Business Performance on Business Models”, “Indian Life
Sciences Business Process Outsourcing (BPO) Industry”,
“Critical Success Factors”, “Critical Success Factors in Life
Science
industry”,
“Critical
Success
Factors
in
Pharma/Biotechnology/Clinical Research industry” and a
combination of these
· ABI / ProQuest
· EBSCO – Business Source Complete
Although quite a bit of work is being conducted under this area of research as
exemplified by the works of Xu et al., (2002); Soh et al., (2000); Ribbers and Schoo
(2002); Scheer and Habermann (2000); Esteves-Sousa and Pastor-Collado (2000);
Bingi et al., (1999); Al-Mashari et al., (2003); Hong and Kim (2002); Somers and
27
Nelson (2001); Umble et al., (2003) there exists no reference to the critical success
factors affecting the Life Sciences BPO Industry.
The observation that there has been no research conducted to date that has a direct
impact on the topic under study, - the effect of business models on business
performance, is a significant finding. To overcome this limitation, an initial pilot
study with a focused sample of professionals from the life sciences BPO industry was
undertaken. Details of the studies undertaken, the process and the results of these
studies are presented under section 3.10 (Chapter 3).
2.3. Organization performance
One of the central functions of entrepreneurship and hence the organization is wealth
creation. According to Knight (1921), entrepreneurs create wealth by purchasing
resources at a price that is lower than their future value, which is uncertain at the time
of purchase. Entrepreneurs are thus focused on the discovery and exploitation of
opportunities for the creation of future goods and services (Shane and Venkatraman,
2000; Venkatraman, 1997).
However, recent work has begun to address the role of planning-related activities
(Delmar and Shane, 2002; Magretta, 2002; McGrath and Macmillan, 2000), in
particular that of design-related tasks (Van de Ven et al., 1984; Hargadorn and
Yellowlees, 2001) as part of the organizational process.
In this study, we build on this emerging literature to examine the impact of business
model design on the performance of entrepreneurial firms or organizations.
Organizational performance has been used widely as the most important criterion in
evaluating organizations; however, researchers often pay little attention to what
performance is and how it is measured (Richard et al., 2008).
There are several challenges researchers must overcome when attempting to measure
organizational performance. First, organizational performance is multidimensional
which makes it difficult to effectively understand its structure, scale, and scope
(Devinney et al., 2005).
28
Next, the relationships between variables of interest (such as business model in this
case) and performance can be influenced by other measures the organization uses
internally and how they alter managerial decisions and actions (Devinney et al.,
2005). Moreover, organizational performance varies over time and it is unclear which
measures vary in which ways (Devinney et al., 2005). In addition, there are practical
issues concerning which measures should be used (e.g., whether subjective vs.
objective measures or financial vs. non-financial measures) (Devinney et al., 2005).
Although associated with the above indicated limitation, organizational performance
is the ultimate dependent variable of interest for researchers concerned with just about
any area of management. This broad construct is essential in allowing researchers and
managers to evaluate firms over time and compare them to rivals. In short,
organizational performance is the most
important
criterion in evaluating
organizations, their actions, and environments.
March and Sutton (1997) found that of 439 articles in the Strategic Management
Journal, the Academy of Management Journal and Administrative Science Quarterly
over a three year period, 23% included some measure of performance as a dependent
variable. In contrast to the dominant role that organizational performance plays in
management fields, is the limited attention paid by researchers to what performance is
and how it is measured.
In 1985, Rawley and Lipson examined the relationships among several combinations
of performance measures to demonstrate that different common measures of financial
performance did not represent the same attributes. Of these comparisons, the only
overall performance measures that they found to be related to each other at
statistically significant levels were the Q ratio versus cash flow return on investment
(“CFROI”) adjusted for the Capital Asset Pricing Model (“CAPM”) discount rate, and
market-to-book value versus return on investment adjusted for inflation.
The Q ratio was proposed by Callard and Kleinman (1985) as a substitute for Tobin’s
Q, and is calculated as the ratio of the value of individual business units divided by
the inflation adjusted purchase cost of assets. The other measures that they compared
were clearly discriminant and do not measure the same construct.
29
Chakravarthy (1986) empirically compared seven exemplar firms with seven
“maladapted” firms in the computer industry, as determined by corporate reputation.
The criteria for selecting the samples were the criteria proposed by Peters and
Waterman (1982) for “excellent” firms. Chakravarthy hypothesized that the means of
the two groups, excellent and non-excellent firms, would differ along common
measures of performance.
Accordingly, those measures of performance that demonstrated that the means of the
two groups were statistically significantly different would be the best measures of
performance for use in strategic management research.
The importance of this research was that no single profitability measure was capable
of discriminating between the two groups of computer firms. This applied to both the
accounting measures used and the market-based measure. As strategic performance
deals with the future, Chakravarthy proposes that a firm needs slack resources to
ensure its flexibility. Accordingly, in assessing strategic performance, the ability of a
firm to produce slack resources is critical.
Brush and VanderWerf (1992) examined thirty-four different studies in the
entrepreneurship literature that explicitly used firm performance as the dependent
variable.
They found that thirty-five different measures of performance were used in those
studies indicating that researchers perceived many different dimensions of
performance, and that there was no agreement on what measures actually represent
overall organizational performance. The most frequently used measures of
performance were changes in sales, organizational survival, changes in number of
employees, and profitability.
Multiple objective measures were much more frequently employed than were
subjective or perceptual measures of performance. Further, the primary means of data
collection was mail surveys, and the primary sources of performance information
were managers, executives, founders or owners.
Robinson (1995) examined ten different new venture performance measures to
determine which individual measure was the most effective in accurately assessing
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long-term economic value creation. Each of the performance measures were
calculated for the three-year period following the firms’ initial public offerings. A
sample of 199 new ventures that had issued an initial public offering prospectus
between 1980 and 1987 were used as the basis of the analysis.
The ten measures studied were (1) change in sales, (2) sales level, (3) return on sales
(“ROS”), (4) return on invested capital (“ROIC”), (5) return on equity (“ROE”), (6)
return on assets (“ROA”), (7) net profit, (8) earnings before interest and taxes
(“EBIT”), (9) earnings multiples, and (10) shareholder value created. Robinson found
strong support for his hypothesis that return to stockholders provided the most
power of the ten measures evaluated in corroborating previously established
relationships between the influence of new venture strategy and the joint influence of
new venture and industry structure on the economic performance of new ventures.
Robinson noted that these results corroborated the prior findings of Ball and Brown
(1968) and Lev and Ohlson (1982).
Murphy, Trailer and Hill (1996) examined the variables used to measure
organizational performance in entrepreneurship research in the years 1987 through
1993. They identified 51 articles published in Academy of Management Journal,
American Journal of Small Business, Entrepreneurship Theory & Practice, Journal of
Business Venturing, and Strategic Management Journal that explicitly used firm
performance as a dependent variable.
They found, consistent with Brush and VanderWerf (1992) and Cooper (1993), that
there was no consistency in the variables used to measure new venture performance.
In total, they identified 71 different dependent variables used to measure performance
in their sample. They subsequently categorized these variables into eight separate
dimensions of performance. They also found that 75% of the sample articles used
primary data sources, 29% used secondary data sources, and only 6% used both. The
high dependence upon primary data sources is typical in Entrepreneurship research,
since there are generally no publicly available financial data sources for non-public
companies. Another finding was that the performance variables used were primarily
financial rather than operational.
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Four primary categories of performance are depicted below since there is no
authoritative list of performance categories in the prior literature, the categories of
performance measures discussed in this chapter are based upon general classifications
of performance measures often found in finance and accounting texts (Brealey, et al.,
2001; Helfert, 1994; Higgins, 1995; Penman, 2001).
The primary variables used in research and practice to represent the overall
organizational performance construct can be categorized into several distinct
groupings. The four primary categories of overall organizational performance
variables used in recent empirical research identified above include (1) accounting
measures, (2) operational measures, (3) market based measures, and (4) survival
measures. In addition, measures of economic value creation are popular in practice
but are not frequently used in strategic management or entrepreneurship research.
ACCOUNTING MEASURES: Accounting measures are those that rely upon
financial information reported in income statements, balance sheets, and statements of
cash flows. Accounting measures can be further subcategorized into profitability
measures, growth measures, leverage, liquidity, and cash flow measures, and
efficiency measures.
Profitability Measures: Profitability measures include values and ratios that
incorporate net income or a component of net income such as operating income or
earnings before taxes. It is through the generation of a profit that an organization is
able to provide a return to providers of equity capital, once the profits have been
converted into liquid assets. In the absence of profits or the likely prospect for profits,
equity capital providers will withdraw their resources from an organization and
redeploy them to alternative investments where a positive return can be realized.
Growth Measures: Growth measures include values and ratios that present some
indication of organizational growth. Growth has been conceptualized both in the
context of resources and from a business operations perspective. Typical accountingbased growth measures include absolute or percentage change in total assets,
operating assets, sales, total expenses, and operating expenses.
Leverage, Liquidity, and Cash Flow Measures: Leverage, liquidity, and cash flow
measures include values and ratios that represent the organization’s ability to meet its
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financial obligations in a timely manner and provide a cash return to capital providers.
The ability to meet financial obligations can be measured both by the ratio of liquid
assets to liabilities, and/or by the organization’s ability to generate sufficient cash
flow to meet outstanding liabilities.
Efficiency Measures: Efficiency measures include values and ratios that represent
how well the organization utilizes its resources. Typical efficiency ratios include asset
turnover, net profit per employee, net profit per square foot, sales per employee, and
sales per square foot. Clearly, most efficiency ratios require information that comes
from outside the three basic financial statements.
OPERATIONAL MEASURES: Operational measures include variables that
represent how the organization is performing on non-financial issues. Measuring
performance on non-financial dimensions has received renewed attention over the
past many years as corporations have adopted a “balanced scorecard” approach for the
integration of strategy and performance measurement (Kaplan, 1984; Kaplan and
Norton 1992). These variables include market share, changes in intangible assets such
as patents or human resources, customer satisfaction, and stakeholder performance.
Most of the measures in this category require primary data from management in the
form of their assessment of their own performance, which may lead to questions of
the validity of the responses.
SURVIVAL MEASURES: Survival measures of performance simply indicate if the
organization remained in business over the time period of interest. Barnard (1938) and
Drucker (1954) proposed that survival is the ultimate measure of long-term
performance. However, since most empirical research in entrepreneurship and
strategic management address time horizons five years and less, survival is rarely
used as a measure of overall organizational performance.
ECONOMIC VALUE MEASURES: Economic value measures of performance are
adjusted accounting measures that take into consideration the cost of capital and some
of the influences of external financial reporting rules. These measures have not been
used by researchers in strategic management or entrepreneurship empirical studies
because the values are not generally reported and most companies do not even
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calculate them internally. Typical economic value measures include residual income,
economic value added, and cash flow return on investment.
MARKET-BASED MEASURES: Market-based measures of performance include
ratios or rates of change that incorporate the market value of the organization.
Examples of these variables include returns to shareholders, market value added,
holding period returns, Jensen’s alpha, and Tobin’s Q. The calculation of these
variables requires a market valuation for the company and is generally only available
for publicly traded companies.
Market-based measures have been hailed as the best possible measures of
organizational economic performance (Copeland et al., 2000; Rappaport, 1986;
Robinson, 1995). They have also been criticized (Bromiley, 1990).
There are several key arguments in favour of market-based measures. First, they
include the value created by both the execution on existing opportunities, as well as
the risk adjusted expected value of future opportunities that have yet to be realized.
Second, and perhaps more important, the issues with accounting-based measures do
not affect stockholder returns (Brush et al., 2000), since accounting measures are
subject to manipulation by management while a well regulated market is generally not
subject to manipulation.
Third, if one accepts the assumption that markets are relatively efficient (and this is
still a matter of considerable scholarly debate), market-based measures quickly reflect
management actions and changes in the economic value of the organization. Also,
since the value of past actions are also quickly incorporated into the market value of
the organization, the change in market value during a given period can be assumed to
reflect the actions taken by management and changes in general market conditions
during that specific time. In contrast, changes in accounting-based measures may lag
managerial actions by considerable periods, which introduces problems for
researchers since intervening events with shorter time lags between action and effect
may also act on accounting-based measures during the lag period in question.
Criticisms of using market-based measures are also numerous. First, under efficient
market theories, changes in returns to capital providers in excess of the weighted
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average cost of capital of the organization are considered surprises to the market. If
the market anticipates an organization’s sales and profit growth correctly, then the
risk adjusted present value of these expectations are already incorporated into the
market value of the organization (Bromiley, 1990). While this assertion is true, it
seems intuitive that entrepreneurship and strategic management researchers are
looking for exactly this information. Specifically, the changes in market value that
researchers are interested in are those that are created by the new actions of
management.
The only way the market could anticipate sales and profitability growth is if there
already existed information, based upon actions already taken by the organization’s
management, which is incorporated in the beginning market value of the organization.
Therefore, market “surprises” must result from new information that becomes
available to the market. Under efficient market theories, this new information must
come from (1) a more complete understanding about the consequences of past
management actions, (2) new actions taken by the organization, or (3) changes in the
organization’s operating environment. Controlling for the external changes in the
organization’s operating environment should result in capturing the effects of firmspecific actions in the market-based measure.
In finance terms, entrepreneurship and strategic management researchers are
interested in unsystematic risk, or the variance in the price of an individual stock that
results from unique circumstances of the company, not the market as a whole
(Brealey, et al., 2001). Bromiley (1990) argues that strategic managers do not manage
stock prices. Managers attempt to influence sales, profits, capital structure, etc. Since
the relationship between these individual measures and changes in stock prices is only
partially understood, the use of changes in stock prices and the associated concepts of
risk are difficult to apply to strategic management research.
Bromiley further argues that stock market returns focus only on the objectives of
shareholders. Many strategic management theorists believe that corporations have
multiple goals (Cyert and March, 1963; Freeman, 1984).
Conversely, finance theory proposes that the market for corporate control results in
management being replaced if they do not act in the best interest of shareholders.
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Accordingly, shareholder goals become primary in the management of for-profit
firms, and managers must make decisions guided by this principle or risk being
replaced. Therefore, maximizing shareholder value, however shareholders define
value, becomes the primary aim of managers.
Based on above literature research the market based measure returns to shareholders
(RTS) was selected in this study to represent business performance. Another reason
why this measure was chosen is that ultimately one of the most critical business
performance factors is what the shareholder gets for his investment. The corporate
governance literature also regards dismissal as the ultimate device to discipline top
management Bushman and Smith (2001); Menon and Williams (2008); Volpin,
(2002) and also poor RTS as one of the major reasons for the ouster of the CFO of
organization.
Dess and Robinson (1984) assert that research involving organizational performance
must address two basic issues: (1) selection of a conceptual framework from which
organizational performance is defined and (2) identification of valid measures to
operationalize organizational performance. In this study we use this approach. The
conceptual framework being the value obtained from the generic business model
framework for the Life Sciences BPO industry and the valid measure selected will be
returns to shareholders (RTS).
2.4. Business Models and Business Performance
Magretta (2002) specifies that a business model should answer the following
questions: Who is the customer? What does the customer value? How do we make
money in this business? What is the underlying economic logic that explains how we
can deliver value to customers at an appropriate cost?
Müller-Stewens and Lechner (2005) adopt the following viewpoint: “A business
model defines how a firm’s particular configuration of the value chain is made
concrete through adoption of a “capitalization perspective”, thereby answering the
question “How do we make money in this business?”: The business model bridges the
gap to operative management by answering the questions: Which services shall be
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offered to which customers? How and within which structure shall these services be
offered? How do I win, foster and keep appropriate customers? How shall the revenue
model be defined concretely?”
Considering the above, it becomes imperative that ultimately a business model should
demonstrate a relationship to business performance. Although the most preferable or
anticipated outcome should be in the positive direction, a negative outcome based on
this relationship would give the organizations a strategic direction on the way forward
to move the direction of business performance outcome from a negative to positive
one.
The next obvious step during this phase of the study was to survey the literature to
identify articles/studies which could throw light on the question of relationship
between business models and business performance. Continuing in this direction
yielded the following studies and their conclusions.
Different theories have been proposed, to explain the difference in performance
among organizations, many of which are aligned with either the “industry view” or
the “firm/organizational view”.
The “industry view” suggests that industry factors, such as market size and barriers to
entry, form the most important explanation for why organizations exhibit different
performance (Porter, 1980). The “firm view” argues that a firms’ endowments and
capabilities, and the difficulty of replicating these, are why firms exhibit performance
heterogeneity (Wernerfelt, 1984).
The empirical literature focuses on disentangling the industry and firm explanations
of performance heterogeneity. (Schmalensee, 1985), using 1975 data on lines of
businesses and reports that industry explains 20% of return on assets (ROA)
heterogeneity, while firm – using market share as a proxy – has negligible explanatory
power.
Rumelt, (1991) uses four years of Federal Trade Commission data and a composite
measure of firm effects. Unlike Schmalensee, he reports that firm (business unit)
effects account for 34 to 46% of explained ROA heterogeneity while industry effects
account for only 8 to 18%, of which about half of this is transient, as measured by the
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interaction of industry effects with year effects. Rumelt also includes a corporateparent effect and finds that it is negligible. This is interpreted as consistent with the
firm view: corporate strategy that structures industry and positions a firm within that
industry does not matter, Carroll (1993), Ghemawat et al., (1993); Hoskisson, (1993).
Roquebert et al., (1996), Brush et al., (1997), McGahan et al., (1997), Chang et al.,
(2000) and Bowman et al., (2001) along with many others evaluate the robustness of
Rumelt’s findings. Other papers agree that firm effects dominate industry effects
Agrawal et al., (1991), Amit et al., (2001), Lubatkin et al., (2001), Mauri et al.,
(1998), McNamara et al., (2003), Powell (1996), Ruefli et al., (2000), Vilmos et al.,
(2006), Walker et al., (2002), but see some differing opinions in Hawawini et al.,
(2005), McNamara et al., (2005).
There is also an important branch of the empirical literature, Denrell (2004),
McGahan et al., (1999), that argues that it is “persistence” that is important, and on
this measure, industry effects dominate.
According to Kaplan et al., (2004), Tapscott et al., (2000), Timmers (1998) and
Slywotzky et al., (1997), a very different explanation, in the form of “business
model,” is commonly offered for why some firms do better than others.
Amit and Zott (2001) identified critical dimensions of business model design, which
they refer to as design themes, and by measuring and quantifying these dimensions,
they showed that: (i) business model design matters to the performance of
entrepreneurial firms, and (ii) business model design themes have a differential
impact on performance under varying environmental conditions. They also discuss on
how their research relates to the findings on the effect of novelty, efficiency, and their
interaction on firm performance by researchers focusing on different levels of
analysis.
Their analysis highlights the business model as an emerging unit of analysis for
entrepreneurship and management research and also provide empirical support for the
suggestion that the design themes of a firm’s business model are determinants of
performance. They are also clear in stating that business models complement, but do
not replace, firm specific and industry specific effects on firm performance (Rumelt,
1991; McGahan and Porter, 1999; Hawawini et al., 2005).
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They also offer the following important implications for practitioners:
·
Corroborate the premise that in a highly interconnected world enabled by
advances in information and communication technologies, entrepreneurs, and
entrepreneurial managers alike may consider looking beyond firm and
industry boundaries in order to create and capture business opportunities.
·
In order to succeed, entrepreneurs need to not only strike a balance between
novel and familiar design elements (Hargadorn and Yellowlees, 2001), but
also find the right mix of design themes (i.e., novelty versus efficiency) in the
sense that there is a need to adapt the design of a business model to a
changing environment.
Some of the limitations indicated in this study include the need to determine the
generalizability of their findings for different types of ventures in different industries
and for firms at different stages of the venture life cycle. They also indicate that the
inclusion of salient business model characteristics, such as design themes, as
independent or dependent variables in research on emerging organizations (Aldrich,
1999), offer the unique opportunity to establish a more clearly defined identity of
entrepreneurship as an independent field of scholarly inquiry.
Chesbrough and Rosenbloom (2002) investigated the role of the business model in
innovation led industries (technology). The biomedical industry survives on
innovation and hence this study help us get a better insight into how business models
affect innovation. They indicate that discovering a viable business model for these
innovations is a critical and neglected dimension of creating value for an innovation
lead organization.
They also offer an interpretation that the business model is a construct that mediates
the value creation process and translates between the technical and the economic
domains, selecting and filtering innovations, and packaging them into particular
configurations to be offered to a chosen target market, essentially what happens in the
biomedical industry.
They also advocate the need for heuristic logic to discover an appropriate business
model for this neglected dimension.
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According to Melone et al., (2006) who formulated a fundamental, reliable and
practical typological definition of business models, classified U.S. firms (10,419
publicly traded United States firms) at the segment level by business model, and
investigated if business models might explain performance heterogeneity, they found
that business model effects are larger than year effects. They also dominate industry
effects, when industry was measured at the comparative (i.e., one-digit NAICS) level.
Their conclusion was robust to very many econometric issues as well as alternative
interpretations.
The organizational performance literature also points out the importance of the
relationship between non-financial and financial organizational performance and how
organizational performance can be justifiably evaluated through perceptual scales.
Therefore, organizational performance was operationalized as non-financial
performance and financial performance and was measured with existing scales found
in the literature (Martinez and Kennerley, 2005; Mausollf and Spence, 2008; Melkers
and Willoughby, 2005).
In summary, it becomes clear that there exists a relationship between business models
and business performance of organizations. Hence determining a specific business
model configuration for the specific organization in a specific industry becomes
critical for its survival and success.
It is also evident that there are no industry specific models, frameworks, tools which
can be applied to create a business model, study effects of varying individual
components on business performance and comparing different organizations with
their own unique business models. Hence there is a dire need to create an industry
specific generic business model framework which can predict business performance
of an organization. This should also provide an option for studying the effect of the
model on performance when constituent business model variables are manipulated.
The above sections conclude the review of literature and support the development of
this research study.
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